L’\u00e9quipe ARMEDIA organise un s\u00e9minaire ouvert \u00e0 tout le laboratoire sur le th\u00e8me assistance \u00e0 domicile pour les personnes \u00e2g\u00e9es.<\/p>\n
Date :<\/strong> jeudi 24 mai de 15h \u00e0 16h<\/p>\n Lieux :<\/strong> Sujet :<\/strong> Par :<\/strong> R\u00e9sum\u00e9:<\/strong> L’\u00e9quipe ARMEDIA organise un s\u00e9minaire ouvert \u00e0 tout le laboratoire sur le th\u00e8me assistance \u00e0 domicile pour les personnes \u00e2g\u00e9es. Date : jeudi 24 mai de 15h \u00e0 16h Lieux : – Evry salle H218 – Palaiseau NanoInnov, salle Visio Sujet : Analyse de l’environnement sonore pour le maintien \u00e0 domicile et la reconnaissance d’activit\u00e9s 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\n– Evry salle H218
\n– Palaiseau NanoInnov, salle Visio<\/p>\n
\nAnalyse de l’environnement sonore pour le maintien \u00e0 domicile et la reconnaissance d’activit\u00e9s de la vie courante, des personnes \u00e2g\u00e9es<\/p>\n
\nMaxime Robin – synth\u00e8se des travaux (th\u00e8se soutenue le 17 avril 2018) en collaboration avec UTC (D. Istrate), KRG (V. Kraus) et TSP (J. Boudy) <\/p>\n
\nL\u2019\u00e2ge moyen de la population fran\u00e7aise et europ\u00e9enne augmente, cette constatation apporte de nouveaux enjeux techniques et soci\u00e9taux. Les personnes \u00e2g\u00e9es \u00e9tant les personnes les plus fragiles et les plus vuln\u00e9rables, notamment du point de vue des accidents domestiques et en particulier des chutes. C\u2019est pourquoi de nombreux projets d\u2019aide au personnes \u00e2g\u00e9es techniques, universitaires et commerciaux ont vu le jour ces derni\u00e8res ann\u00e9es.
\nCe travail de th\u00e8se a \u00e9t\u00e9 effectu\u00e9e sous convention Cifre, conjointement entre l\u2019entreprise KRG Corporate et le laboratoire BMBI (Biom\u00e9canique et Bioing\u00e9nierie) de l\u2019UTC (Universit\u00e9 de technologie de Compi\u00e8gne). Il a pour objet de proposer un capteur de reconnaissance de sons et des activit\u00e9s de la vie courante, dans le but d\u2019\u00e9toffer et d\u2019am\u00e9liorer le syst\u00e8me de t\u00e9l\u00e9-assistance d\u00e9j\u00e0 commercialis\u00e9 par la soci\u00e9t\u00e9. Plusieurs m\u00e9thodes de reconnaissance de parole ou de reconnaissance du locuteur on d\u00e9j\u00e0 \u00e9t\u00e9 \u00e9prouv\u00e9es dans le domaine de la reconnaissance de sons, entre autres les techniques : GMM (Mod\u00e8le de m\u00e9lange gaussien \u2013 Gaussian Mixture Model), SVM-GSL (Machine \u00e0 vecteurs de support, GMM-super-vecteur \u00e0 noyau lin\u00e9aire \u2013 Support vector machine GMM Supervector Linear kernel) et HMM (Mod\u00e8le de markov cach\u00e9 \u2013 Hidden Markov Model). De la m\u00eame mani\u00e8re, nous nous sommes propos\u00e9s d\u2019utiliser les i-vecteurs pour la reconnaissance de sons. Les i-vecteurs sont utilis\u00e9s notamment en reconnaissance de locuteur, et ont r\u00e9volutionn\u00e9 ce domaine r\u00e9cemment. Puis nous avons \u00e9largi notre spectre, et utilis\u00e9 l\u2019apprentissage profond (Deep Learning) qui donne actuellement de tr\u00e8s bon r\u00e9sultats en classification tous domaines confondus. Nous les avons tout d\u2019abord utilis\u00e9 en renfort des i-vecteurs, puis nous les avons utilis\u00e9 comme syst\u00e8me de classification exclusif.
\nLes m\u00e9thodes pr\u00e9c\u00e9demment \u00e9voqu\u00e9es ont \u00e9galement \u00e9t\u00e9 test\u00e9es en conditions bruit\u00e9s puis r\u00e9elles. Ces diff\u00e9rentes exp\u00e9rimentations nous ont permis d\u2019obtenir des taux de reconnaissance tr\u00e8s satisfaisants, les r\u00e9seaux de neurones en renfort des i-vecteurs et les r\u00e9seaux de neurones seuls \u00e9tant les syst\u00e8mes ayant la meilleure pr\u00e9cision, avec une am\u00e9lioration tr\u00e8s significative par rapport aux diff\u00e9rents syst\u00e8mes issus de la reconnaissance de parole et de locuteur.<\/p>\n","protected":false},"excerpt":{"rendered":"